在现象学和神经层面上,GPT-4V显示了类似人类的社会感知能力
Severi Santavirta1,2, Yuhang Wu1,3, Lauri Suominen1,2
1Turku PET Centre, University of Turku, Turku, Finland.
Imaging neuroscience (Cambridge, Mass.)
|September 8, 2025
概括
像GPT-4V这样的大型语言模型 (LLM) 展示了类似人类的能力,可以在图像和视频中感知和注释社会特征. 这种能力扩展到模拟人类大脑对社会刺激的反应,开辟新的研究途径.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 人类在社会感知方面表现出色,这是一个复杂的认知功能.
- 大型语言模型 (LLM) 在视觉理解方面取得了进展.
- 从视觉数据中推断社会信息的LLM的潜力尚未被探索.
研究的目的:
- 调查LLM是否可以从图像和视频中推断出复杂的社会信息.
- 将LLM生成的社会特征注释与人类评估进行比较.
- 评估LLM注释是否与人类社会感知结构和大脑反应保持一致.
主要方法:
- 在社交电影场景的图像和视频上收集了GPT-4V的社会特征评估.
- 将GPT-4V注释 (N=468张图像,N=234张视频) 与人类评估 (N=2,254) 进行了比较.
- 模拟的血液动力学反应 (N=97) 的社会情绪刺激使用人类和GPT-4V注释.
主要成果:
- 在注释个人社会特征方面,GPT-4V实现了类似人类的能力.
- GPT-4V的社会特征注释显示了与人类感知相似的结构表示.
- 基于LLM的刺激模型揭示了人类社会感知网络,与基于人类的模型相比.
结论:
- 法学士在视觉媒体中展示了理解和注释复杂的社会信息的巨大潜力.
- 这种类似人类能力的LLMs在各种领域都有广泛的应用,并推进了心理学/神经科学研究.
- 基于LLM的模型为研究人类社会大脑提供了一个新的工具.
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